The described related work tracks motion using optical flow algorithms. It seems that those produce satisfying results but not yet cover the full potential of a AR tracking system. Others use interest-point based algorithms which are commonly known as very computational expensive. SIFT descriptors are probably the most used ones although they might belong the… Continue reading Review: „Multiple Target Detection and Tracking with Guaranteed Framerates on Mobile Phones“
Tag: SIFT
Experiment #1 - JavaSIFT
Just tried to do a first experiemnt using the ImageJ plugin JavaSIFT. I took nine pictures* of my house with my smartphones camera. The goal was to let JavaSIFT reigster some interest points and then to see what I can do with that (JavaSIFT has some "align images" function). Turned out that the plugin is… Continue reading Experiment #1 - JavaSIFT
Rapid Object Detection using a Boosted Cascade of Simple Features
[...] a machine learning approach for visual object detection which is capable of processing images extremely rapidly and achieving high detection rates violaJones_CVPR2001.pdf (application/pdf-Objekt).
SURF
SURF (Speeded Up Robust Features) is a robust image detector & descriptor, first presented by Herbert Bay et al. in 2006, that can be used in computer vision tasks like object recognition or 3D reconstruction. It is partly inspired by the SIFT descriptor. The standard version of SURF is several times faster than SIFT and… Continue reading SURF